When it comes to value-based healthcare, the primary objective is to improve patient outcomes while reducing costs. Unlike traditional healthcare models, where providers are compensated based on the quantity of services provided, value-based models link reimbursement to patient wellness and the complexity of their medical conditions.
This paradigm shift underscores the importance of accurately documenting and coding the nuances of a patient’s health through tools like HCC coding. Without it, providers may struggle to secure the compensation they deserve, highlighting the challenges inherent in this patient-focused approach.
This blog discusses HCC coding under value-based reimbursement models, which is crucial for disease identification and risk adjustment coding, ensuring high-quality care and sustainable profitability.
HCC Coding: An Elaborative Definition
HCC coding, short for Hierarchical Condition Category, is a risk-adjustment model predicting patient risks and lifetime healthcare costs. As healthcare shifts to value-based models, the importance of HCC coding grows. It assigns risk scores by connecting HCC codes to patient demographics, allowing for risk-adjusted payments. HCC codes, based on ICD-10-CM codes, help ensure fair compensation for patients with varying health conditions.
The Working Principles of HCC Codes
ICD-10 codes require detailed information about a patient’s care and medical conditions, including chronic issues like alcohol dependence or artificial openings. These details help predict future healthcare needs and are crucial in determining Hierarchical Condition Categories (HCCs), which can forecast a patient’s future well-being. Risk factors like these influence the patient’s payment scale. The more conditions a patient has, the higher their health risk, resulting in an HCC score above 1000. Conversely, healthier patients score below 1000.
However, providers sometimes overlook specifying HCCs in various scenarios, leading to incomplete risk scores that don’t fully reflect the patient’s health status. The RAF score in both models is determined by combining demographic data and diagnoses identified by ICD-10 codes.
Demographics include factors such as age, gender, and, for the CMS-HCC model, the place of residence (either in a community or a skilled nursing facility), along with Medicare and/or Medicaid enrollment status.
Moreover, within the CMS-HCC risk adjustment model, there are two risk segments:
- The community model.
- The institutional model (as outlined in the Advance Notice of Methodological Changes for Calendar Year 2020 for the Medicare Advantage CMS-HCC Risk Adjustment Model).
Medicare uses the community model for most beneficiaries, while the institutional model is designed for individuals who have resided in nursing homes or assisted living facilities for extended periods.
The CMS HCC risk adjustment V24 model includes 86 HCC group categories for chronic illnesses. Some of the most common chronic conditions in Medicare patients fall under these categories, including:
- Hypertension
- Hyperlipidemia
- Arthritis
- Diabetes
- Ischemic heart disease
- Chronic Kidney Disease
- Depression
- Heart Failure
- COPD
- Alzheimer’s / Dementia
- Atrial Fibrillation
In the CMS/HCC risk adjustment model, specialists determine payments by splitting them equally between two criteria: the Payment Condition Count model, which accounts for the number of medical conditions each patient has, and the risk adjustment criteria.
The Role of HCC Codes in Risk Adjustment Programs
The HCC risk adjustment model by CMS calculates patients’ risk score (RAF) based on their health, symptoms, and demographic details, using ICD-10-CM codes mapped to HCC codes. Patients with higher RAFs receive increased compensation for managing chronic conditions. However, the CMS resets the risk adjustment calendar each year on January 1. Without fresh diagnosis codes on claims, patients are considered healthy.
Securing Precise Reimbursement
To ensure coding accuracy, CMS conducts regular risk adjustment data validation (RADV) audits. Inaccurate or incomplete documentation can lead to underpayments and disrupt the revenue cycle, causing delays in claims processing and increased patient follow-up. Incorrect coding can also result in penalties for not meeting HEDIS standards, requiring additional patient visits for proper assessment.
A study reported by Health Leaders found that correcting coding errors could significantly increase a primary care physician’s annual revenue, adding over $124,000 in preventive services and $86,000 in coordination services. Accurate and comprehensive documentation is crucial for appropriate reimbursement and compliance.
The Fundamental Need for Precise HCC Coding and Its Importance
Accurate and thorough clinician documentation of chronic condition diagnoses is crucial for the risk adjustment and HCC processes. Providers must record all conditions assessed during each face-to-face visit. It is also vital that the documentation is as detailed as possible.
The level of specificity can significantly impact the patient’s treatment plans and the accuracy of code assignments, ultimately leading to proper HCC classification and appropriate reimbursement.
Coding professionals must thoroughly review the medical record to assign the correct ICD-10-CM diagnosis codes. Most chronic conditions correspond to an HCC, and the documentation must clearly establish the presence of the disease or condition to support an HCC.
Additionally, it should include the clinical provider’s assessment and/or plan for managing the condition. Many organizations follow the “M.E.A.T.” criteria for Monitoring, Evaluation, Assessment, and Treatment. This acronym guides documentation practices, ICD-10-CM coding, and HCC assignments in the medical field.
Capturing Conditions Accurately
For example, if the documentation only notes diabetes mellitus without specifying any complications, the HCC classification shifts from HCC 18 (diabetes with chronic complications) to HCC 19 (diabetes without complications). The comparative risk factor for HCC 18 is 0.344, whereas for HCC 19, it decreases to only 0.109.
This lack of specificity may result in inaccurate data submission, leading to a decline in the overall raw risk score from 1.920 to 1.685 and a corresponding drop in predicted payments from $17,981 to approximately $15,784—a reduction of $2,197 for just one patient. With 27% of Medicare beneficiaries having diabetes, many of whom likely have chronic complications, failing to capture these details could result in substantial overall payment losses.
Barriers in the Domain of HCC Coding
As the U.S. healthcare system shifts to value-based payment models, independent clinicians face HCC coding challenges that impact finances and patient care. The past pandemic has exacerbated the strain further, which has added to the workload. Here are four major HCC coding challenges clinicians currently face and strategies to overcome them:
- Enhancing Physician Training for Accurate HCC Coding – Physicians know HCC coding is crucial but lack engaging, effective training methods, making learning challenging amidst their already demanding responsibilities.
- Revenue Impact Due to Incorrect Coding – Inaccurate HCC coding affects reimbursements, patient care, and RAF scores, underscoring the need for well-trained clinicians in this complex coding system.
- Poor HCC Integration with EMR Systems – Lack of HCC integration with EMR complicates workflows, increases errors, and burdens clinicians, necessitating systems that support accurate documentation and seamless integration.
- Lack of Trained HCC Coding Professionals – Staffing shortages in HCC coding affect both small practices and larger groups, highlighting the need for comprehensive training of clinicians and non-clinical staff.
Leading Practices in HCC Coding for Maximum Efficiency
- Effective Provider Engagement: Overcoming provider engagement challenges involves tailored education, prospective outreach, and fostering a culture that values accurate coding to involve healthcare providers actively.
- Comprehensive Document Scope: Accurate coding requires expanding the document scope to include all patient documents like ancillary reports, labs, pathology, radiology reports, and operative notes to avoid missing critical information.
- Accurate Documentation: Essential for accurate coding, documentation must be complete and adhere to the MEAT standard (Monitoring, Evaluation, Assessment, Treatment), with specific details on diseases or conditions.
- Leverage Natural Language Processing (NLP): NLP streamlines coding by identifying missing information, providing auto-suggestions, and efficiently capturing critical details, saving time and improving coding accuracy.
- Reporting and Dashboards: Reports and dashboards help manage populations, improve performance, and track provider effectiveness, offering insights and enabling data-driven decisions in healthcare organizations.
- HCC Revenue Forecast: Forecasting HCC revenue highlights reimbursement potential, motivates provider engagement in HCC programs, and underscores the importance of accurate coding for maximizing value.
- Role of AI: AI technology like Neuro-Symbolic AI, which facilitates Computer-Assisted Coding (CAC) or CAPC, expedites the coding process, enhances accuracy, and offers real-time dashboards for ongoing improvements in healthcare coding.
By implementing the above best practices for provider engagement, utilizing technology, expanding document scope, and leveraging AI tools, hospitals and health systems can effectively navigate challenges to ensure accurate coding and enhance reimbursement rates.
The Path Ahead for HCC Coding
The future of HCC coding looks promising as CMS refines its risk score models. AI and machine learning advancements will introduce new tools that automate coding, ensuring accuracy and compliance with CMS standards. These technologies will reduce errors, enhance interoperability, and improve data sharing,
ultimately supporting better patient care and cost management through continuous updates and provider education. Automating parts of the coding process will reduce errors and save time. Enhanced interoperability and data sharing will improve coding accuracy.
Continuous updates to guidelines, ongoing provider education, and supportive policies will ensure high coding standards, promoting effective use of HCC coding for better patient care and cost management.
Conclusion
As healthcare shifts to value-based models, accurate HCC coding is crucial for fair compensation and patient outcomes. Leveraging NLP and AI tools can enhance coding precision and efficiency. Adopting best practices, such as comprehensive training and detailed documentation, is essential for value-based and high-quality patient care success.
